Research

“Antitrust Risk and Voluntary M&A Disclosure” (Dissertation)

This study examines whether antitrust risk affects firms’ disclosure of mergers and acquisitions (M&As). Due to regulatory exemptions, deals that fall below a size threshold escape formal antitrust scrutiny at the time of the merger. These “non-reported” deals can have important implications for the firm’s pricing power of its products in segmented and localized markets. I hypothesize that firms face a trade-off between the benefits of disclosing these deals to capital markets (i.e., capitalize the product market benefits into stock prices in a timely manner) and the potential antitrust scrutiny the voluntary disclosure can invite, which increases the probability of agencies challenging the merger. Exploiting two quasi-exogenous variations in antitrust enforcement that affect the level of antitrust risk, I find evidence of acquirers strategically managing their disclosure of horizontal and intrastate deals according to the level of antitrust risk. My findings shed light on a new determinant of voluntary disclosure: the risk of costly antitrust enforcement.

• Presented at: Cornell University, PhD Poster Session at Nicholas Dopuch Accounting Conference, KAAPA PhD Rookie Camp, Miami Rookie Camp, 2023 FARS, Boston College, Columbia University, Harvard University, HKUST, MIT, National University of Singapore, Singapore Management University, University of British Columbia, University of Hong Kong, University of Illinois at Chicago, University of Michigan, 2023 MIT Asia Accounting Conference

“Technology Coopetition and Voluntary Disclosures of Innovation” with P. Eric Yeung

We examine firms’ voluntary disclosures of innovation under technology coopetition, focusing on technology standard setting organizations (SSOs). Technology coopetition is characterized by i) cooperation to determine technology standards, which requires information sharing to reach consensus, and ii) competition for standard implementation to obtain standard-essential patents, which creates incentives for firms to deviate from the expected level of information sharing. We document a decrease in 10-K narrative R&D disclosures, more generic 10-K narrative R&D disclosures, and longer delay of patent disclosures to the USPTO after a firm joins an SSO. Among alternative explanations, our evidence is more supportive of the hypothesis that firms strategically withhold innovation information.

• Presented at: Cornell University, 2021 AAA Doctoral Consortium, 2022 HARC, 2022 LBS TADC, 2022 MIT Asia Accounting Conference, 2022 CFEA, 2023 FARS
• Conditionally accepted at The Accounting Review

“Media Conglomeration, Local News, and Capital Market Consequences” with Travis Dyer and Mark H. Lang

We examine the effect of news media consolidation on local business coverage and its consequences for firms, investors, and capital markets. Using television transcripts, we find a substantial drop in coverage of local firms following the consolidation of local television stations associated with Sinclair acquisitions. Local information search, institutional portfolio local bias, and overall retail trading decrease for firms in treated geographic areas. Results are most pronounced for small firms, for stations with higher ex-ante viewership and for stations with greater reductions in local coverage. Our results provide insight into the consequences of media consolidation for local business coverage, firms, investors, and capital markets.

• Presented at: 2021 BYU Accounting Research Symposium*, Cornell University, 2024 HARC
• Accepted at Management Science

“Contract Contingencies and Uncertainty: Evidence from Product Market Contracts” with Kai Wai Hui, Guoman She, and P. Eric Yeung

We study contingencies written in firms’ material product market contracts, focusing on the theoretical prediction of uncertainty being an important determinant. We identify contract contingencies from firms’ public regulatory filings and examine the effects of general business uncertainty and specific innovation-related uncertainty. For causality, we utilize two major business shocks (the 2008 financial crisis and the COVID pandemic) and the diffusion of 29 disruptive innovation shocks (Bloom et al. 2021). We also explore the effects of re-negotiation costs and writing costs. Overall, our empirical results are consistent with the predictions from the dynamic models of incomplete contracting.

• Presented at: Cornell University*, 2022 AAA Annual Meeting, Boston College*, 2023 FARS

“Does Algorithmic Trading Affect Forced CEO Turnover?” with Jaewoo Kim, Hojun Seo, and Luo Zuo

We examine whether algorithmic trading (AT) affects the extent to which directors rely on stock returns when making CEO turnover decisions. We find that the sensitivity of forced CEO turnover to stock returns decreases with AT. This effect of AT is more pronounced when the information that AT crowds out is more likely to be new to directors, when there is more informed trading that AT dampens, when the directors’ expertise allows them to extract decision-relevant information from stock returns, and when the directors’ own information set is poor. In contrast, the effect of AT does not vary with the strength of firms’ corporate governance. Further analysis suggests that directors rely more on non-market measures and meet more frequently as AT increases. Overall, our findings suggest that directors incorporate information in stock returns regarding the CEO-firm match quality into their CEO turnover decisions and that AT deters such learning.

• Presented at: Purdue University*, Washington University in St. Louis*, KAIST-Korea University Joint Workshop*, University of Oregon*, 2023 HARC*, 2023 FARS*, 2023 AAA Annual Meeting*

Presentation by co-author (*)